{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6e6d00a4e2b7145",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T05:59:35.463067Z",
     "start_time": "2024-04-21T05:59:35.163790Z"
    }
   },
   "outputs": [],
   "source": [
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88d01bedb973cc3a",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:00:00.158935Z",
     "start_time": "2024-04-21T06:00:00.088876Z"
    }
   },
   "outputs": [],
   "source": [
    "# a\n",
    "tripleshift <- c(-1, 1, 3)\n",
    "# 三个数据集的三个中心mean shift\n",
    "classlabel <- c(\"c1\", \"c2\", \"c3\")\n",
    "cols <- 50\n",
    "rows <- 20"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "set.seed(123)\n",
    "simulate1 <- data.frame(matrix(round(rnorm(rows * cols, mean = tripleshift[1]), 3),\n",
    "                    nrow = rows), name = classlabel[1])\n",
    "simulate2 <- data.frame(matrix(round(rnorm(rows * cols, mean = tripleshift[2]), 3),\n",
    "                    nrow = rows), name = classlabel[2])\n",
    "simulate3 <- data.frame(matrix(round(rnorm(rows * cols, mean = tripleshift[3]), 3),\n",
    "                    nrow = rows), name = classlabel[3])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:00:26.196932Z",
     "start_time": "2024-04-21T06:00:26.167247Z"
    }
   },
   "id": "861b619214dc1d42",
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/html": "<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n</style>\n<ol class=list-inline><li>60</li><li>51</li></ol>\n",
      "text/markdown": "1. 60\n2. 51\n\n\n",
      "text/latex": "\\begin{enumerate*}\n\\item 60\n\\item 51\n\\end{enumerate*}\n",
      "text/plain": "[1] 60 51"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combinedataset <- data.frame(rbind(simulate1, simulate2, simulate3))\n",
    "dim(combinedataset)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:00:35.757368Z",
     "start_time": "2024-04-21T06:00:35.735362Z"
    }
   },
   "id": "5e509d86b2ca15ca",
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Importance of components:\n                          PC1     PC2     PC3     PC4     PC5     PC6     PC7\nStandard deviation     6.0693 0.97932 0.95359 0.90124 0.83345 0.82369 0.80396\nProportion of Variance 0.7367 0.01918 0.01819 0.01624 0.01389 0.01357 0.01293\nCumulative Proportion  0.7367 0.75591 0.77410 0.79034 0.80424 0.81781 0.83073\n                           PC8    PC9    PC10    PC11    PC12    PC13    PC14\nStandard deviation     0.79436 0.7779 0.75026 0.72335 0.69237 0.66588 0.64937\nProportion of Variance 0.01262 0.0121 0.01126 0.01046 0.00959 0.00887 0.00843\nCumulative Proportion  0.84335 0.8555 0.86671 0.87718 0.88677 0.89563 0.90407\n                          PC15    PC16   PC17    PC18    PC19    PC20    PC21\nStandard deviation     0.62476 0.59856 0.5874 0.57720 0.55504 0.51996 0.51210\nProportion of Variance 0.00781 0.00717 0.0069 0.00666 0.00616 0.00541 0.00524\nCumulative Proportion  0.91187 0.91904 0.9259 0.93260 0.93876 0.94417 0.94942\n                          PC22    PC23    PC24    PC25    PC26    PC27    PC28\nStandard deviation     0.49308 0.46525 0.45340 0.44890 0.41873 0.39607 0.38454\nProportion of Variance 0.00486 0.00433 0.00411 0.00403 0.00351 0.00314 0.00296\nCumulative Proportion  0.95428 0.95861 0.96272 0.96675 0.97026 0.97339 0.97635\n                          PC29    PC30    PC31    PC32    PC33    PC34   PC35\nStandard deviation     0.36654 0.34768 0.33740 0.32957 0.31136 0.29392 0.2650\nProportion of Variance 0.00269 0.00242 0.00228 0.00217 0.00194 0.00173 0.0014\nCumulative Proportion  0.97904 0.98146 0.98373 0.98590 0.98784 0.98957 0.9910\n                          PC36    PC37    PC38    PC39    PC40    PC41   PC42\nStandard deviation     0.25923 0.23871 0.23725 0.23185 0.19574 0.19242 0.1728\nProportion of Variance 0.00134 0.00114 0.00113 0.00108 0.00077 0.00074 0.0006\nCumulative Proportion  0.99232 0.99346 0.99459 0.99566 0.99643 0.99717 0.9978\n                          PC43    PC44    PC45    PC46    PC47    PC48    PC49\nStandard deviation     0.15502 0.14547 0.14280 0.13491 0.11044 0.07853 0.07379\nProportion of Variance 0.00048 0.00042 0.00041 0.00036 0.00024 0.00012 0.00011\nCumulative Proportion  0.99825 0.99867 0.99908 0.99944 0.99968 0.99981 0.99992\n                          PC50\nStandard deviation     0.06459\nProportion of Variance 0.00008\nCumulative Proportion  1.00000"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# b\n",
    "dataset1 <- combinedataset[1: 50]\n",
    "pcadataset <- prcomp(dataset1, scale = T)\n",
    "summary(pcadataset)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:00:52.792675Z",
     "start_time": "2024-04-21T06:00:52.757697Z"
    }
   },
   "id": "148607744cf191e3",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/html": "<table class=\"dataframe\">\n<caption>A data.frame: 60 × 1</caption>\n<thead>\n\t<tr><th scope=col>name</th></tr>\n\t<tr><th scope=col>&lt;chr&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c1</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c2</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n\t<tr><td>c3</td></tr>\n</tbody>\n</table>\n",
      "text/markdown": "\nA data.frame: 60 × 1\n\n| name &lt;chr&gt; |\n|---|\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c1 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c2 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n| c3 |\n\n",
      "text/latex": "A data.frame: 60 × 1\n\\begin{tabular}{l}\n name\\\\\n <chr>\\\\\n\\hline\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c1\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c2\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\t c3\\\\\n\\end{tabular}\n",
      "text/plain": "   name\n1  c1  \n2  c1  \n3  c1  \n4  c1  \n5  c1  \n6  c1  \n7  c1  \n8  c1  \n9  c1  \n10 c1  \n11 c1  \n12 c1  \n13 c1  \n14 c1  \n15 c1  \n16 c1  \n17 c1  \n18 c1  \n19 c1  \n20 c1  \n21 c2  \n22 c2  \n23 c2  \n24 c2  \n25 c2  \n26 c2  \n27 c2  \n28 c2  \n29 c2  \n30 c2  \n31 c2  \n32 c2  \n33 c2  \n34 c2  \n35 c2  \n36 c2  \n37 c2  \n38 c2  \n39 c2  \n40 c2  \n41 c3  \n42 c3  \n43 c3  \n44 c3  \n45 c3  \n46 c3  \n47 c3  \n48 c3  \n49 c3  \n50 c3  \n51 c3  \n52 c3  \n53 c3  \n54 c3  \n55 c3  \n56 c3  \n57 c3  \n58 c3  \n59 c3  \n60 c3  "
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combinedataset[51]\n",
    "datasetname <- factor(unlist(combinedataset[51]))\n",
    "# 列表格式无法直接用factor()转换，需要先用unlist()转化为向量"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:01:05.064074Z",
     "start_time": "2024-04-21T06:01:05.029406Z"
    }
   },
   "id": "f0ac2a8f50fb7540",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "plot without title",
      "image/png": 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7GMZbgkIcHZACqwr0NSf4XvexNv/wdp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\nwP8PXvzQ+IqwXRgAAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "width": 420,
       "height": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "biplot(pcadataset, scale=0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:01:39.734076Z",
     "start_time": "2024-04-21T06:01:39.621124Z"
    }
   },
   "id": "d1c738cd60b3429a",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "plot without title",
      "image/png": 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8B6bqX+XbxF8bju4s/snZ/bTHY/rn6+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\nAvB/H6R29MXQJRcAAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "width": 420,
       "height": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(pcadataset$x, aes(x=PC1, y=PC2, color = datasetname)) + geom_point()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:02:04.396457Z",
     "start_time": "2024-04-21T06:02:04.159328Z"
    }
   },
   "id": "27fccd107270b47d",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "\n 1  2  3 \n20 20 20 "
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# c\n",
    "result1 <- kmeans(dataset1, 3, iter.max = 50, algorithm = \"Lloyd\")\n",
    "table(result1$cluster)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:02:33.240942Z",
     "start_time": "2024-04-21T06:02:33.219400Z"
    }
   },
   "id": "995cc7953313fbc",
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "plot without title",
      "image/png": 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KladwsShIQ2VetuQYKQ0KZq3S1IEBLaVK27BQlCQpuqdbcgQUhoU7XuFiQICW2q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\nP6YiPGBh859LAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "width": 420,
       "height": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustercolor <- factor(result1$cluster)\n",
    "ggplot(pcadataset$x, aes(x=PC1, y=PC2, color = clustercolor)) + geom_point()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:02:44.016516Z",
     "start_time": "2024-04-21T06:02:43.740835Z"
    }
   },
   "id": "c338a750d2216603",
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "\n 1  2 \n40 20 "
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# d\n",
    "result2 <- kmeans(dataset1, 2, iter.max = 50, algorithm = \"Lloyd\")\n",
    "table(result2$cluster)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:02:59.870826Z",
     "start_time": "2024-04-21T06:02:59.856050Z"
    }
   },
   "id": "97b7cc96d3f8400",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "plot without title",
      "image/png": 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swEkSytTNWTmcSItMRrpC5a06ZWG1BE\njkjLuGv3flXUw9QxSxFpi/Ewdt66v0/XwdRRSxFpi/Ew9t16+M5R/tRxSxFpi/Ew9t2KSO2t\n1QYUMVCk9RsffzIexr5bEam9dRwPmhkm0vrwyxbjYey8lWuk5tZxPGgGkaZt5a5da+s4HjSD\nSJZWpurJTL4t0j9vjDcHoE/4jGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbq\nyUx4ZoOllal6MhOea2dpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQydLK\n1M0v0sv6VRtQBCJZWpn6R32h2WoDikAkSytT5Zc+H3Zanz+v/12f/c/D4s8Pq9X908XIsOZT\nKg7jCHTUylSLSBf+86B/97zacskkRLK0MrU7kR5Wj6+vj6sflyLDmk+pOIwj0FErU/+Mco30\n/HO1fnw3YWfD9tf/rVc//t38fvunv29foz383f7d7/X9R2j7xdvD8z509KfNP1t/1CFSTCtT\nN7+U37X7u9648vOzSI/bL8r+3Yu0/Uc/tn93v3r4CO1+s/67Cx3/afPP3n3hM1JUK1P15BUe\n3074p71EHyKtVs9v/3m9//S0+xpt+xnq8Tj0uHr79HS/+W+f//S4/x/4tYkhUk4rU/XkFX6s\n/r6f1icirVcPvw7/+e0fbX/7cyfYUejH5o/Pm885m393/Kfn9/7nzVeCiBTUylQ9eYXDFcyp\nSL/evkr7cbj2Wb1zchX18Ztzn9Bue4RInlam6snvi/T6+vvHav3UKtLvKx4hkqeVqXryCl+/\ntHveW/Dv4T/9OJzzu78b8KXd9q//23w1iEhZrUzVk1d4XD2+/t4bs1799/r3fve7p7f/vL/Z\nsPlHb1bc7w05hC7fbNj8s6fVlc9HiGRqZaqevMLz/s724ab3/z5uf/9v85/X+/vaq997Qw6h\ny7e/N//s/vAVISLltDJVT17j9/3um6i7Tz3rN3n2v1u/efT29d3myXaHJ829W7EPXf6G7NYU\nRApsXeDU0+++8vNIG5oPakfnUfbZOUFrUemn5wMh0obmo9rReRR9dk7RWlP6+RmqiLSh+bB2\ndB4ln52TtCLSIBDJ0rq4qYh0jubD2tF5lHx2TtLKNdIgEMnSusCp3LX7SvNB7eg8yj47J2hN\nm1ptQBGIZGllqp7M5ESkpx/bn8h9vfXD7cbDOJNWpurJTI6Fedo+BWL71DxEGreVqXpSPC8r\nbLnCsTDbp7r+2v7QBSKN28pUPSmelyW6XOZYmP2TYe8RaexWpupJ8byssOUKX0V6M+nnGCLd\n3d19fFBSwy06amWqnhTPywJZrnEszMP7q6X8Xt3Xi3R3d2TS67FVZaQ95FOXLmOqel4WyHKN\nY2GeV4cfqi0X6e7u2KQTq8pIe8inLl3GVPW8LJDlGifCPD+8v8j40/2oIp1adT31jf+NtId8\n6tJlTFXPy2ZVrjPVN2Qlkb75iSvtIf/M0ZNk0qeOXYpIW5SP/8tXdrcVGfyJa3+olF3TtR4/\nbTN86uilcxdp9/4xTw+/r4ekA3CsxDc+Ic1GpJMfJMieOn7phdYhLwaunpetptzgRKT77Wur\nbF6+6PH8v35HOnQnH9TQKyRE+g69izTo7SnU8/LyaV/CceN/q/XuJZKfNi8Jpg4ewsCGWV0j\nIdKt1mFvmDT4vPxUdXLWjyvS/erX++9+XX8xvLZj+Gf4gzOru3ZcI91orRXpc9fJST+uSEee\n8hShEVq5a3e9tVSkL2UXT/saLol09V1rRzmM7XTUytRz/7HyGskp0v3H+8Ac3qHsLK1Hsafz\nqPuzM7J0/Lt2TpH+PejzcLhaOkvbMfzT1XnU/9mZWDrB95GuXCONLdLrevVz85LITz8vvlPm\nmcHSwWhu6LyVqXpy6Hl55a7d2CLtXpd/8xr8z5f+/ZnBysFobui8lal6UjwvL5/2JXxq/O/n\n5g2er34T6RWRQkuXMVU9L6+d9gXwKkKWVqbqSfG8bD/tr4JIllam6knxvBQFGcqJSId3XLqB\nfBAOH1RzQ+etTNWT4nnZKMotjkX6vdq/J+ANpAPAazaMXbqMqep52azKdb68ZsPD9e/FblE+\n/tPXbFAabtJRK1P1pHheNqtynS9PEfp7/dlBW4QP//RHItIenMlbmaonxfOy1ZQbfH2u3YCn\nmAsfPiKNX7qMqep52SjKLRDJ0vpROuTpZd9vLSTtqKrnZaMot5hKJK6RzpcOesLzt1srSTuq\n6nnZKMotJhOJu3bnSof9CM53W0tJO6rqedkoyi1ORTriWkg+CIcPqrmh81ZE0pPieVnjy0UQ\nydKKSHpSPC9rfLkITxGytHKNpCfF81IUZCiIZGnlrp2eFM9LUZChIJKllal6UjwvRUGGEiJS\nyZtTpD3kU5cuY6p6XoqCDCVDpJq3eUl7yKcuXcZU9bwUBRlKhEjffW3iYa1FLOLsnLoUkbbI\nB+HwQZ3+EZGCW9OmquelKMhQEMnSylQ9KZ6Xxyf9je+TKkSIxDVScGva1MHn5adT6tM5X21S\n/atASLx90O4J0D2fPTox6eOf5Ygk/3+TwwfV3NB5K1P15LDz8svlQsWJfwVEsrQyVU8OOy8R\naRGtTNWTw87LGyLN9GZDDR21MlVPDjwvL18jyef9NRDJ0spUPTn0vLx410497a+CSJZWpupJ\n8bxsPuuvg0iWVqbqSfG8bD3pb4BIllam6knxvDw652/+CLgAIllamaonxfNSFGQoiGRpZaqe\nFM9LUZChIJKllal6UjwvRUGGgkiWVqYe/+Fbr1uhnpeiIENBJEvrwqZ+NeX19G+/YZJ6XoqC\nDAWRLK3LmnrGlNdPfzvcJPW8FAUZCiJZWhc19ZwpiLThe8fx3AfV3NB566KmItIlvnccz31Q\nzQ2dty5q6g2R6q6RnCCSpXVZU69fI9XdtXOCSJbWhU29ftfue1QbUAQiWVqZqiczQSRLK1P1\nZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpm5/Vd6usNqAIhDJ0srUzS/S\nG+hWG1AEIllamfpHfUv3agOKQCRLK1P/IBIihZb2NhWRSg5jPR21MnXzC9dIJYexnI5ambr9\nlbt2JYexmo5amaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZINL0rW+X2L1MHasUkbYYD+MM\nWqWbvoNAJB+INHWr9m3IQSCSD0SauhWR2lqrDSgCkaZuRaS21moDikCkyVu5RmpqrTagCESa\nvpW7doi0w3gYZ9LKVD2ZCSJZWpmqJzNBJEsrU/VkJog0UevpDYboqROUItIW42HstfXTrbrk\nqVOUXm29flOz2oAiEGmS1s/fPAqeOknptdYb3x6oNqAIRJqkFZEGt976hnW1AUUg0iStiDS4\nFZFKDuNcW7lGGtqKSCWHcbat3LUb2so1UslhXEYrU6/8HXftSg7jIlqZqiczQSRLK1P1ZCaI\nZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0Ek\nSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZSZJI\nd3d3I7S2soizs6x06PsDINIG+SBcPYx3d40mzfbstLZ+q3TwO20sV6T10e/lg3DtMN7dtZo0\n17PT2/qd0uHv/bRYkdaIFF/qn4pIt1jzGSm/1D8VkW4zukhcI2W2co00iG+L9M8bI21582ik\nZpiKzbsRLpKgz0hLamWqnswEkSytTNWTmdwSab1+v1+HSPGly5g6mgpt8BnJ0spUPZkJIlla\nmaonM0EkSytT9WQmQc+1W1IrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVPZoJI\nllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0Sy\ntDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKl\nlal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWs+UDn331e+1tpN2VKsNKCJCpNZ3YT7f\nWsREZ+fgdxb6VmsBaUe12oAiEkRqfoexs61VTHN2Dn+vu++0VpB2VKsNKCJApPb3vDzXWgYi\nZbVWG1AEIllaEUlPZoJIllaukfRkJgEicY20hbt2A5OZJIjEXbvk1rSp1QYUESFSER21MlVP\nZoJIllam6slMEMnSyjWSnswEkSyt3LXTk5kgkqWV7yPpyUwQydKKSHoyE0SytCKSnswEkSyt\nXCPpyUwQydLKXTs9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0Sy\ntDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKl\nlal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWl8LfozvTGt9Zd5RrTagCESytFb8YPlX\nEMkHIjlaS17q5CuI5AORHK2IhEgbjIdxHq2IhEgbjIdxJq1cI+nJTBDJ0spdOz2ZyXQiHb+b\nWNqDM3krU/VkJpOJdPL+lmkPzritZz77pE6dqhSRtggf/uk7Lqc9OKO2nrseCp06Tum5L2MR\naYPw4S9XpLN36DKnjlN69sYKIm0QPnxEKi09T6RI52/1I9IG5eNf7DUSIiHSJaQDsNi7dgu/\nRkKky7Qd2abD2GPrwu/acY0EUMGbR+4J48NnJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWp\nejITRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzV\nk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqe\nzASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/Vk\nJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqZqicz\nQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJ\nIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQ\nydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVPZoJI\nllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0Sy\ntDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKl\nlal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJgNFWr/x8SfjYZxJ\nK1P1ZCbDRFofftliPIwzaWWqnswEkSytTNWTmXzjGgmRwkuXMXUECSr4tkj/vDHSFoBuGS4S\nNxvSS5cxdQQJKkAkSytT9WQmt0Q63Pc+8giRMkuXMXU0FdoY+hnp2CNEyixdxtQRJKhg6Ddk\nT/5kPIwzaWWqnsxk4PeR1idPbTAexpm0MlVPZsJz7SytTNWTmSCSpZWpejITRLK0MlVPZoJI\nllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0Sy\ntDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKl\nlal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSyt\nTNWTmSCSpZWpejITRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGll\nqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT\n9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWpmq\nJzNBJEsrU/VkJpfhwg0AAASESURBVIhkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVPZoJI\nllam6slMEMnSylQ9mQkiWVoXO/Xl5aWxtdqAIhDJ0rrUqS8v7yYh0obm49nRedTB2Tlua2Xp\ny8veJETa0HxAOzqP8s/OkVsRaRCIZGld6FREOqX5gHZ0HuWfnSO3co00CESytC52Knftjmk+\nnB2dRz2cnaO2pk2tNqAIRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaI\nZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2YiiQQAp/AZydLKVD2ZCSJZWpmqJzNBJEsr\nU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqZ\nqiczQSRLK1P1ZCaIZGllqp7MBJEsre+l+1d5q22tJe2oVhtQBCJZWnelh9cdLW0tJu2oVhtQ\nBCJZWrelH6+EXdlaTdpRrTagCESytCKSnswEkSytiKQnM0EkSyvXSHoyE0SytHLXTk9mgkiW\nVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0\nMlVPZoJIllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWV\nqXoyE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M\n1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWq\nnswEkSytTNWTmSCSpZWpejITRLK0MlVPZiKJFMo/7gHDYercQCQLTJ0biGSBqXMDkSwwdW7M\nSSQAG4gEUAAiARSASAAFIBJAAYgEUMCcRFpvcI8YQi87Xzs6pm5mJZJ7wEDWh1/y6WSmH0Sa\nHkSaITMSqZvHvCOR+liZwJxE6uXL+Z5E6uWY2pmTSIdfwulmaFdTzcxIpC09POi9nZ0dTfUx\nC5GOvv7o4UFHpBkyC5F2dHN+djO0q6lmZiZSF495R2dnN8fUzoxE6ucJA90M7WqqlzmJBGAD\nkQAKQCSAAhAJoABEAigAkQAKQCSAAhAJoABEAigAkQAKQKSxWe14+L3747/3q9X9f/u/fOT4\nzwQeyLFZ7dmY9Lze/f5+93ePK47/TOCBHJt3Vx638qxXD8+vr7/Wq383/+1hhUhzgQdybPau\nbP7vf6uf29//Wm1/PmH9hEhzgQdybI5F+rl62v1he8H0+PGX0Ds8kGOzc+Xvw+rhjDeINBd4\nIMfmcLPhGZFmDA/k2OwsWm9vfyPSbOGBHJtjVw7XSK9PX/8SeoYHcmyOXdnftXtaP3z9S+gZ\nHsixOXHl8H2k32f+EjqGB3JsTlx5/rG7ZHo895fQMTyQY/PJlV8P66Pn2iHSXOCBBCgAkQAK\nQCSAAhAJoABEAigAkQAKQCSAAhAJoABEAigAkQAKQCSAAhAJoID/A9AcDLyUClRaAAAAAElF\nTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "width": 420,
       "height": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustercolor2 <- factor(result2$cluster)\n",
    "ggplot(pcadataset$x, aes(x=PC1, y=PC2, color = clustercolor2)) + geom_point()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:03:07.563860Z",
     "start_time": "2024-04-21T06:03:07.423686Z"
    }
   },
   "id": "dbeea3d73acf01a0",
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "\n 1  2  3  4 \n 9 11 20 20 "
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# e\n",
    "result3 <- kmeans(dataset1, 4, iter.max = 50, algorithm = \"Lloyd\")\n",
    "table(result3$cluster)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:03:23.794557Z",
     "start_time": "2024-04-21T06:03:23.774563Z"
    }
   },
   "id": "4fa988dd74bcd5fa",
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "plot without title",
      "image/png": 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JG6fwZe2kv+aSn3SEpSNeH6f276c98W\nj8+/F1ffpcWIVPA54Wkv+eelPLUTkrd4+r5Y/ng1YW/D7sf/LXd/K9u+S3n51b+HxeLh3+73\n/izv30PPT9vfeHoLHf1q98cObYiU08pUPXmDf8utK9/PRfrx+reyV5F2f+jb7vfuFw/vof1P\nlv/2oeNfbf/Ylt+LH4gU1cpUPXmDHy8H/vFNoneRFounl/+8fHv3tLXhx2L3HurHcejH4uX9\nzv32v53/6lWf71vd0kWa4z3SKFrTpt46mN8W/16P9YlIy8XD78N/fvlDu59+3wt2FPq2/eXT\n9n3V9s8d/+rp1dNv103KEWkeT+1e74rGMLXP0p5EOtzCnIr0++Vvad8O9z6LV07uot5/cukd\n2uF/4NfVv9sFiTSH1rfndCOY2mvpsCI9P//5tlg+Foh0/WkDIg3ZevjIUf7UfksH+6vd09vJ\n/3n4T98OZ37/ew1/tdv+7nLxD5FSWhGpe+utg/nj5W9ef96MWS5+Pf+73//s8eU/vz1s2P6h\nl7+j3b9ZcQhdf9iw/2P/2+r40E2k5QuI1BlE6t5665g+vT3ZPjz0/t/74+//bf/z8u259uLP\nmyGH0PXH39s/9vrrp04iLQ8/IFInuEfq3HrzoP65338Qdf+uZ/kiz9vPli8evbxD2R7i7Uda\n7x+fD39Pewtd/4Ds3tK3XyNSQCtP7bq2Nh3Y4UEkSytT9WQmXxbpvxf6mwMwTniPZGllqp7M\nBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTPjMBksrU/VkJnyunaWVqXoyE0SytDJV\nT4rnUhSkFUSytDJVT4rnUhSkFUSytDJVT4rnUhSkFUSytDJVT4rnUhSkFUSytDJ1+8NmsxGS\n4rkUBWkFkSytTP2780gwST2XoiCtIJKllamvHn3dJPVcioK0gkiWVqYi0jMihZaObCoiIVJm\n6dimco8kXryjN6pzw8hbmbr9gad2XRnRORrd6RxFqfvjSNKxvwkiWVqZqidbz+XZ+7uTU49I\nE2llqp5sPJfnd2Anhx6RJtLKVD3Zdi4/PBPsfuxvgkiWVqbqybZziUizaGWqnmw7l4g0i1am\n6snGc3njHgmRJtPKVD3Zei5vPLVDpKm0znDqarUqaVXPZfdjfxNEsrTOb+pqdWISIm2RL8Lh\njercMPLW2U1drU5NQqQt8kU4vFGdG0beOrupYSLVg0iW1tlNRaRLyBfh8EZ1bhh56/ymZt0j\n1YNIltYZTo16alcPIllamaonxXMpCtIKIllamaonxXMpCtLKiUiP3xbffu7/803B5ItweKM6\nN4y8lal6UjyX3V25ybEwj4st97v/jEi9tjJVT4rnssKWGxwLc7/48fz8e7k1CZH6bWWqnhTP\nZYku1zkWZm/P09YkROq3lal6UjyXFbbc4KNILyZ970Oku7u79zdKaviMEbUyVU+K57JAllsc\nC/Ow/avdC38W9/Ui3d0dmfR8bFUZaS/50KXzmKqeywJZbnEszNPi1Z9fi3KR7u6OTTqxqoy0\nl3zo0nlMVc9lgSy3OBHm6eH1Oy4/3vcq0qlVt1Nf+N9Ie8mHLp3HVPVcdlblNkN9QFYS6Yvv\nuNJe8nPW63V96TGIpOUqGOwzGz78ze5zRZrfcb1dKmXXcK3r9btJ4VN7L526SD93f7V7fPhz\nOyRdgGMlvvAOaTIirddHJmVP7b/0SmvLPwaunsuupnzCiUj3i8VOoeXr47trSJfu5I1qvUNC\npK8wdpGavj2Fei6PD/0nD9MUjgt/LZa/dz95XC5+3QqJF+/ojWr7Y5O6R0Kkz1rbvmFS87k8\n/bqNo9zi/OBXcPopQr9ff/Z7/xl3bYMFWhsm9dSOe6RPWmtFOvtKwiFFOnp/x6cI9dDKU7vb\nraUinX9t+3luKJGWt0K9XMbujKiVqZf+Y+U9klOk+8XT68+eFg+3Ql2v4pjO0ehPZ2Rp/0/t\nPhGp14cNPw/6PBzuli7S7Rr+HdU5Gv/pTCwd4ONI1++Rzs99CSeFy8X3x5f/8/h98e1mSL4I\nhzeqc8PIW5mqJ1vP5dWndufHvoTTz7VbLvYsn679+QuDlYvRuWHkrUzVk+K5vHrqazir/PX9\nRaPvNz+I9IxIoaXzmKqey+uHvgT+FSFLK1P1pHguj878oodPbUAkSytT9aR4LkVBWjkR6c/9\nYvFw+/Zoh3wRDm9U54aRtzJVT4rnsqMon3Es0p/9+7xPPvX7mX+zIbR0HlPVc9lZldt8+Dcb\nHm5/LHaH8vaf/psNSsOnjKiVqXpSPJedVbnNh08R+nf7s4N2CG/+6ZdEpL04g7cyVU+K57Kr\nKZ/w8XPtGp5nCG8+IvVfOo+p6rnsKMpnIJKl9b306FPCC1sLSbuq6rnsKMpnDCUS90iXS4+/\nSKmutZK0q6qey46ifMZgIvHU7lLpyZfNlrWWknZV1XPZUZTPOBXpiFsh+SIc3qjODSNvRSQ9\nKZ7LGl+ugkiWVkTSk+K5rPHlKnyKkKWVeyQ9KZ5LUZBWEMnSylM7PSmeS1GQVhDJ0spUPSme\nS1GQVkJEKvnmFGkv+dCl85iqnktRkFYyRKr5Ni9pL/nQpfOYqp5LUZBWIkT66r9N3NZaxCxO\n59CliLRDvgiHN+r0l4gU3Jo2VT2XoiCtIJKllal6UjyXx4e+539Evxn5IhzeqLNfc4+U25o2\ntflcnh2pszMf8W821PPyRrsnwOg59+jEpPc/liOS/P9NDm9U54aRtzJVT7adyw+3CxUH/waI\nZGllqp5sO5eINItWpurJtnN5U6TJPmyoYUStTNWTjefy+j2SfvBvgEiWVqbqydZzefWpnX7w\nb4BIllam6knxXJ6deUSaRCtT9aR4Ls/OPCJNopWpelI8l8eHnocNU2llqp4Uz6UoSCuIZGll\nqp4Uz6UoSCuIZGllqp4Uz6UoSCuIZGll6vEvWr6Z+XtSPJeiIK0gkqV1ZlPPvi/yWetm8xWT\n1HMpCtIKIlla5zV1tfpg0lHrZvMlk9RzKQrSCiJZWmc1dbX6aBIibfnadbz0RnVuGHnrrKYi\n0jW+dh0vvVGdG0beOqupn4jEPZLOiM5R6ukcrLX3eySe2umM6BzFns6hWvt/avc11HMpCtIK\nIllamaonxXMpCtIKIllamaonxXMpCtIKIllamaonxXMpCtIKIllamaonxXMpCtIKIllamaon\nxXMpCtIKIllamaonxXMpCtIKIllambr78eNj8YakeC5FQVpBJEsrU7c/XPhAbUNSPJcF5/4W\niGRpZerfy5861JBsPZdn35339Ngj0jRamfq3b5HOv1/86alHpGm0MvVvzyKt12cmnR56RJpG\nK1O3P/R5j4RIs2hl6u7HHp/a3RBpoZ77WyCSpZWperLxXF69R+rlH1pFJE8rU/Vk67m89tRu\nsUc5+DdAJEsrU/WkeC4Lzv0tEMnSylQ9KZ7LgnN/C0QavvXl7xxjmdpXKSLtkC/C4Y3q3DDi\n1vO74DoQSctVgEhDt354LlsHImm5ChBp6FZE6taqnktRkFYQaehWROrWqp5LUZBWEGnwVu6R\nOrWq51IUpBVEGr6Vp3aItEe+CIc3qnPDyFuZqifFcykK0goiWVqZqiczQSRLK1P1ZCaINFDr\n2aci15SegUg+EGmY1vMvjikpPWciIt3+KqVqA4pApEFazz94FDx1kNJbrZ983Wy1AUUg0iCt\niNTc+tm/5FBtQBGINEgrIjW3IlLJZZxqK/dIra2IVHIZJ9vKU7vWVu6RSi7jPFqZeuP3eGpX\nchln0cpUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9m\ngkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejIT\nRLK0MlVPZoJIllam6slMkkS6u7vrobUrszidZaWbzaaH1pNkJkEi3d11NGmyp9Pa+qXSzabR\npPmKtDz6uXwRbl3Gu7uuJk31dHpbv1K62bSaNFuRlogUX+qfikifseQ9Un6pfyoifU7vInGP\nlNnKPVITXxbpvxd62vLiUU/NMBQvHrkneAh6jzSnVqbqyUwQydLKVD2ZyWciLZevz+sQKb50\nHlN7U6EbvEeytDJVT2aCSJZWpurJTBDJ0spUPZlJ0OfazamVqXoyE0SytDJVT2aCSJZWpurJ\nTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9m\ngkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejIT\nRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrRdK1+t1\nD63dSbuq1QYUESFS1+/CfLm1iIFO53rd3SRE8pEgUufvMHaxtYphTud6XWASIvkIEKn797y8\n1FoGImW1VhtQBCJZWhFJT2aCSJZW7pH0ZCYBInGPtIOndo3JTBJE4qldcmva1GoDiogQqYgR\ntTJVT2aCSJZWpurJTBDJ0so9kp7MBJEsrTy105OZIJKllY8j6clMEMnSikh6MhNEsrQikp7M\nBJEsrdwj6clMEMnSylM7PZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6\nMhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWT\nmSCSpZWpejITRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqfC76M70JrfWXeVa02oAhEsrRW\nfGH5RxDJByI5Wkv+qZOPIJIPRHK0IhIibTFexmm0IhIibTFexom0co+kJzNBJEsrT+30ZCbD\niXT83cTSXpzBW5mqJzMZTKST72+Z9uL023rhvU/q1KFKEWmH8OaffsfltBen19ZL90OhU/sp\nXa1Wha3VBhSBSH23XnxClzm1n9LV6oJJiLRFePMRqbT0MpEirVaXTEKkLcrbP9t7JERCpGtI\nF2C2T+1mfo+ESNfpdmU7XcYxts78qR33SAAVvHjkntA/vEeytDJVT2aCSJZWpurJTBDJ0spU\nPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbq\nyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVP\nZoJIllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoy\nE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZ\nIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswE\nkSytTNWTmSCSpZWpejITRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaI\nZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0Ek\nSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZ\nWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVPZoJIllam6slMEMnS\nylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2bSKNLy\nhfdfGS/jRFqZqiczaRNpefhhh/EyTqSVqXoyE0SytDJVT2byhXskRAovncfUHiSo4Msi/fdC\nT1sARku7SDxsSC+dx9QeJKgAkSytTNWTmXwm0uG595FHiJRZOo+pvanQjdb3SMceIVJm6Tym\n9iBBBa0fkD35lfEyTqSVqXoyk8aPIy1PPrXBeBkn0spUPZkJn2tnaWWqnswEkSytTNWTmSCS\npZWpejITRLK0MlVPZoJIllam6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEs\nrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRp\nZaqezASRLK1M1ZOZIJKllWnofOcAAAVTSURBVKl6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2Z\nCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVPZoJIllam6slM\nEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aC\nSJZWpurJTBDJ0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNE\nsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCS\npZWpejITRLK0MlVPZoJIllam6slMEMnSOtupq9WqY2u1AUUgkqV1rlNXq1eTEGlL5+s5onM0\ngtPZb2tl6Wr1ZhIibel8QUd0jvJPZ8+tiNQEIllaZzoVkU7pfEFHdI7yT2fPrdwjNYFIltbZ\nTuWp3TGdL+eIztEYTmevrWlTqw0oApEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTBDJ\n0spUPZkJIllamaonM0EkSytT9WQmiGRpZaqezASRLK1M1ZOZSCIBwCm8R7K0MlVPZoJIllam\n6slMEMnSylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJV\nT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0EkS+tr6Xq97qG1lrSrWm1AEYhkad2Xrte1JiGS\nD0SytO5K1+tikxDJByJZWhFJT2aCSJZWRNKTmSCSpZV7JD2ZCSJZWnlqpyczQSRLK1P1ZCaI\nZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZWpurJTBDJ0spUPZkJIllamaonM0Ek\nSytT9WQmiGRpZaqezASRLK1M1ZOZIJKllal6MhNEsrQyVU9mgkiWVqbqyUwQydLKVD2ZCSJZ\nWpmqJzNBJEsrU/VkJohkaWWqnswEkSytTNWTmSCSpZWpejITRLK0MlVPZoJIllam6slMEMnS\nylQ9mQkiWVqZqiczQSRLK1P1ZCaIZGllqp7MBJEsrUzVk5kgkqWVqXoyE0SytDJVT2aCSJZW\npurJTBDJ0spUPZkJIllamaonM5FECuU/94B2mDo1EMkCU6cGIllg6tRAJAtMnRpTEgnABiIB\nFIBIAAUgEkABiARQACIBFDAlkZZb3CNaGMvO5xFdUzeTEsk9oJHl4Yd8RjLTDyINDyJNkAmJ\nNJrXfEQijWNlAlMSaSx/nR+TSGO5pnamJNLhh3BGM3RUU81MSKQdY3jRx3Y6RzTVxyREOvr7\nxxhedESaIJMQac9ozudoho5qqpmJiTSK13xEp3M019TOhEQazycMjGboqKZ6mZJIADYQCaAA\nRAIoAJEACkAkgAIQCaAARAIoAJEACkAkgAIQCaAAROqbxZ6HP/tf/rxfLO5/vf3mD67/ROCF\n7JvFG1uTnpb7n9/vf+/Hgus/EXgh++bVlR87eZaLh6fn59/Lxc/tf3tYINJU4IXsmzdXtv/3\n1+L77ue/F7uvT1g+ItJU4IXsm2ORvi8e97/Y3TD9eP9NGDu8kH2zd+Xfw+LhgjeINBV4Ifvm\n8LDhCZEmDC9k3+wtWu4efyPSZOGF7JtjVw73SM+PH38TxgwvZN8cu/L21O5x+fDxN2HM8EL2\nzYkrh48j/bnwmzBieCH75sSVp2/7W6Yfl34TRgwvZN+cufL7YXn0uXaINBV4IQEKQCSAAhAJ\noABEAigAkQAKQCSAAhAJoABEAigAkQAKQCSAAhAJoABEAijg/+bsQc1OPfvMAAAAAElFTkSu\nQmCC"
     },
     "metadata": {
      "image/png": {
       "width": 420,
       "height": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustercolor3 <- factor(result3$cluster)\n",
    "ggplot(pcadataset$x, aes(x=PC1, y=PC2, color = clustercolor3)) + geom_point()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:03:33.389095Z",
     "start_time": "2024-04-21T06:03:33.150023Z"
    }
   },
   "id": "3a9e902c142782f1",
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "\n 1  2  3 \n20 20 20 "
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# f\n",
    "result4 <- kmeans(pcadataset$x, 3, iter.max = 50, algorithm = \"Lloyd\")\n",
    "table(result4$cluster)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:03:50.197583Z",
     "start_time": "2024-04-21T06:03:50.182670Z"
    }
   },
   "id": "cab63710faff2b47",
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "plot without title",
      "image/png": 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EhLvEfqojVtarUBReSItIyndi93RT1M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\nARSASAAF/B88xJITJzLKEwAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "image/png": {
       "width": 420,
       "height": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustercolor4 <- factor(result4$cluster)\n",
    "ggplot(pcadataset$x, aes(x=PC1, y=PC2, color = clustercolor4)) + geom_point()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:03:57.980884Z",
     "start_time": "2024-04-21T06:03:57.809850Z"
    }
   },
   "id": "d5667712dae86b40",
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "\n 1  2  3 \n20 20 20 "
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# g\n",
    "scaledataset <- scale(combinedataset[1: 50], scale = T)\n",
    "result5 <- kmeans(scaledataset, 3, iter.max = 50, algorithm = \"Lloyd\")\n",
    "table(result5$cluster)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:04:14.416870Z",
     "start_time": "2024-04-21T06:04:14.388411Z"
    }
   },
   "id": "9180d72f3eef03b6",
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "plot without title",
      "image/png": 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d1ut4Wt1QYUgUhjt158Qpc5dZzS7faCSYi0Q3jzEam09DKRIm23l0xCpB3K27/Y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\nTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "width": 420,
       "height": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustercolor5 <- factor(result5$cluster)\n",
    "ggplot(pcadataset$x, aes(x=PC1, y=PC2, color = clustercolor5)) + geom_point()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-21T06:04:22.525899Z",
     "start_time": "2024-04-21T06:04:22.385643Z"
    }
   },
   "id": "4a3e3d861e69933",
   "execution_count": 18
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
